Schizophrenia detection technique using multivariate iterative filtering and multichannel EEG signals

Abstract

A new approach for extension of univariate iterative filtering (IF) for decomposing a signal into intrinsic mode functions (IMFs) or oscillatory modes is proposed for multivariate multi-component signals. Additionally the paper proposes a method to detect schizophrenia (Sz), based on analysing multi-channel electroencephalogram (EEG) signals. Using proposed multivariate iterative filtering (MIF), multi-channel EEG data are decomposed into multivariate IMFs (MIMFs). Depends on mean frequency, IMFs are grouped in order to separate EEG rhythms (delta, theta, alpha, beta, gamma) from EEG signals. The features, such as Hjorth parameters are extracted from EEG rhythms. Extracted features are ranked using student t-test and most discriminant 30 features are used for classification. Different classifier such as K-nearest neighbours (K-NN), linear discriminant analysis (LDA), support vector machine (SVM) with diffident kernels are considered to classify Sz and healthy EEG patterns. The proposed method is employed to evaluate 19-channel EEG signals recorded from 14 paranoid Sz patients and 14 healthy subjects. We have achieved highest accuracy of 98.9% using the SVM (Cubic) classifier. Sensitivity, specificity, positive predictive value (PPV), and area under ROC curve (AUC) of the same classifier are 99.0%, 98.8%, 98.4% and 0.999 respectively. Proposed approach for MIF is computationally efficient as compared to other multivariate signal decomposition algorithms. This paper presents a framework for decomposing multivariate signals efficiently and builds a model for detecting Sz accurately. © 2021 Elsevier Ltd